Leveraging Big Data and Machine Learning for Real-Time Economic Monitoring and Forecasting K. Sindhura, Navaneetha Krishnan Rajagopal, Wasib Bin Latif, J. Ravi Kumar, Ravindra Asharam Kayande, Senthilkumar Sakthivel Proceedings of the 2025 International Conference on Technology Enabled Economic Changes Intech 2025, 2025 This research investigates the efficacy of multiple machine learning models in real-time macroeconomic monitoring and forecasting, utilizing synthetic time series data. We investigated five models: Linear Regression, Random Forest, GBM, LSTM, and Transformer, focusing on their usefulness in anticipating economic patterns. The models were analyzed based on MAE, MSE, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{\mathrm{2}}$</tex> metrics. Our findings demonstrate that the GBM model surpasses other strategies, displaying the lowest MAE and MSE and the largest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{\mathrm{2}}$</tex> score, suggesting its superior capacity to characterize intricate non-linear interactions. The Random Forest model also demonstrated strong performance, with lower error metrics and a substantial <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{\mathrm{2}}$</tex> score, suggesting its efficacy in identifying subtle patterns in the data. The Linear Regression approach, while less successful at controlling non-linear trends and noise, still supplied a meaningful baseline for comparison. The LSTM and Transformer models, developed for sequential data, displayed comparable performance, with Transformers marginally exceeding LSTM in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{\mathrm{2}}$</tex>. Both models accurately depicted temporal dependency but highlighted some weaknesses in dealing with unpredictable trends and noise. Box-and-whisker plots indicated that GBM consistently provided lower variability in performance parameters, but LSTM and Transformer models had essentially identical performance levels, with Transformer demonstrating much greater results. The paper underlines the promise of complex machine learning models for fiscal forecasting, explaining the effectiveness of GBM and Transformer in processing intricate time series data. Future work needs to concentrate on hyperparameter tuning, real-world data application, hybrid exhibiting methodologies, and enhancing scalability for realtime monitoring. This research proposes a framework for utilizing strong machine learning capabilities to boost the accuracy and reliability of economic forecasts.
Human Resource Management based Economic analysis using Data Mining Sindhura K, A. Sabarirajan, Kumar Siddamallappa U, Pravin Narang, Mahesh Manohar Bhanushali, Anjaneya Krishna Turai Proceedings of 3rd International Conference on Intelligent Engineering and Management Iciem 2022, 2022 Researchers in this field are looking at how data mining and HRM are intertwined to manage human resources better. An Ensemble Classifier-Decision Tree (EC-DT) approach may analyse Human Resource Management (HRM) data. C4.5, Random Tree, J48, and Simple Cart are the single decision tree algorithms employed. Based on the algorithm design, algorithms for evaluation and talent recommendation are then assessed in an HRM system. The algorithm under development is put through its last paces in terms of comparisons and testing. The EC-DT method has a classification accuracy of 79.97 %, whereas C4.5 has a classification accuracy of 76.69 %. DMRM's accuracy and recall were 35.2 % and 41.6 % better than CRM's and CFR's combined, proving that data mining may be a powerful tool for recommending products and services (CFRM). Thus, the data mining-based human resources management system may help organisations expand due to quantitative analyses. In this way, the data mining-based HRM system may encourage and direct businesses to grow under the outcomes of quantitative assessments. A starting point for further research on data mining-based HRM systems may be found in the abovementioned findings
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